Bayesian Inference in Dynamic Disequilibrium Models : An Application to the Polish Credit Market

We review Bayesian inference for dynamic latent variable models using the data augmentation principle. We detail the difficulties of simulating dynamic latent variables in a Gibbs sampler. We propose an alternative specification of the dynamic disequilibrium model which leads to a simple simulation procedure and renders Bayesian inference fully operational. Identification issues are discussed. We conduct a specification search using the posterior deviance criterion of Spiegelhalter, Best, Carlin, and van der Linde (2002) for a disequilibrium model of the Polish credit market